Marily Nika: recommendation

Transcript context

…What are signs that AI may not be a good approach to solving a problem? You said that, and this happened on a lot of my teams, oh we're going to build a really cool model, it's going to do something really smart in this case and it often ended up being a very low ROI investment and took six months to a year before you even knew what the hell what was happening. Do you have any thoughts on signs that maybe this isn't a place you should be putting a lot of time into AI versus this is definitely an opportunity. Yes, we should do this, invest a lot of time into this. Don't do it for your MVP. It makes zero sense. Do not waste time of data scientists that can train models with using powerful machines that are going take weeks to train. This is because if you have an MVP and you just want to get buy-in for an idea or feature that may use AI in the future, take it, create a little figma prototype and just show it some users, just fake what the AI is going to be doing. So a lot of young early stage entrepreneurs reach out to me and they say, "Oh, should train this model to do this and that because we want to prove that there is a market." "No, do not use AI. You should use AI where you think you already have some data or data from an adjacent product that you feel you can leverage for your own product to create something that's meaningful, recommendation automation, what we talking about. But not for an MVP. Please people, this is my advice. How much data do you think you need for AI ML to have a chance to contribute? You have a heuristic of if you have anything less than this, it's not going to work at all.…

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